Search for composite and exotic fermions at LEP 2

A search for unstable heavy fermions with the DELPHI detector at LEP is reported. Sequential and non-canonical leptons, as well as excited leptons and quarks, are considered. The data analysed correspond to an integrated luminosity of about 48 pb^{-1} at an e^+e^- centre-of-mass energy of 183 GeV and about 20 pb^{-1} equally shared between the centre-of-mass energies of 172 GeV and 161 GeV. The search for pair-produced new leptons establishes 95% confidence level mass limits in the region between 70 GeV/c^2 and 90 GeV/c^2, depending on the channel. The search for singly produced excited leptons and quarks establishes upper limits on the ratio of the coupling of the excited fermio

Search for lightest neutralino and stau pair production in light gravitino scenarios with stau NLSP

Promptly decaying lightest neutralinos and long-lived staus are searched for in the context of light gravitino scenarios. It is assumed that the stau is the next to lightest supersymmetric particle (NLSP) and that the lightest neutralino is the next to NLSP (NNLSP). Data collected with the Delphi detector at centre-of-mass energies from 161 to 183 \GeV are analysed. No evidence of the production of these particles is found. Hence, lower mass limits for both kinds of particles are set at 95% C.L.. The mass of gaugino-like neutralinos is found to be greater than 71.5 GeV/c^2. In the search for long-lived stau, masses less than 70.0 to 77.5 \GeVcc are excluded for gravitino masses from 10 to 150 \eVcc . Combining this search with the searches for stable heavy leptons and Minimal Supersymmetric Standard Model staus a lower limit of 68.5 \GeVcc may be set for the stau mas

Updated precision measurement of the average lifetime of B hadrons

The measurement of the average lifetime of B hadrons using inclusively reconstructed secondary vertices has been updated using both an improved processing of previous data and additional statistics from new data. This has reduced the statistical and systematic uncertainties and gives \tau_{\mathrm{B}} = 1.582 \pm 0.011\ \mathrm{(stat.)} \pm 0.027\ \mathrm{(syst.)}\ \mathrm{ps.} Combining this result with the previous result based on charged particle impact parameter distributions yields \tau_{\mathrm{B}} = 1.575 \pm 0.010\ \mathrm{(stat.)} \pm 0.026\ \mathrm{(syst.)}\ \mathrm{ps.

Particle Swarm Optimization approach for fuzzy cognitive maps applied to autism classification

The task of classification using intelligent methods and learning algorithms is a difficult task leading the research community on finding new classifications techniques to solve it. In this work, a new approach based on particle swarm optimization (PSO) clustering is proposed to perform the fuzzy cognitive map learning for classification performance. Fuzzy cognitive map (FCM) is a simple, but also powerful computational intelligent technique which is used for the adoption of the human knowledge and/or historical data, into a simple mathematical model for system modeling and analysis. The aim of this study is to investigate a new classification algorithm for the autism disorder problem by integrating the Particle Swarm Optimization method (PSO) in FCM learning, thus producing a higher performance classification tool regarding the accuracy of the classification, and overcoming the limitations of FCMs in the pattern analysis area. © IFIP International Federation for Information Processing 2013

Implementing fuzzy cognitive maps with neural networks for natural gas prediction

The goal of this research study is to test the hardiness of a novel hybrid computational intelligence model in day-ahead natural gas demand prediction. The proposed model combines an evolutionary learned FCM method with a common ANN to construct a cascaded model that leads to high prediction accuracy in most distribution points. The FCM technique is used to provide a model which concepts are used as input nodes in a second-stage ANN model employed to provide the forecast for each gas time series. Learned by structure optimization genetic algorithm, the FCM outputs are fed into an ANN to refine the initial forecast and upgrade the overall forecasting accuracy. The model is applied to five distribution points that compose the natural gas grid of a Greek region, district of Thessaly. This approach enables the comparison of the hybrid model performance on different FCM and ANN structures and on consumption patterns, providing also insights on the characteristics of large urban centers and small towns. © 2018 IEEE

Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors. © 2022 by the authors

A software tool for FCM aggregation employing credibility weights and learning OWA operators

In this study, we present the functionalities of a new tool for FCMs using credibility weights and OWA-based operators for aggregation tasks. The average aggregation method for weighted interconnections among concepts is the most used method in FCM modeling. The aim of this research work is to (i) propose an alternative aggregation method based on learning OWA operators in aggregating FCM weights, assigned by many experts and/or stakeholders and (ii) to estimate and rank the experts' credibility using a distance-based method. The applicability and usefulness of the proposed methodology in modeling and decision-making is demonstrated using poverty eradication strategies under DAY-NRLM (Deendayal Antyodaya Yojana-National Rural Livelihoods Mission) of India. The results produced by the proposed learning OWA operators are compared with the known average aggregation method of FCMs. These results imply that the proposed alternative FCM aggregation approach is really challenging when a large number of experts and stakeholders are engaged to design the overall FCM model. © 2019 IEEE

Evaluating the effectiveness of climate change adaptations in the world's largest Mangrove Ecosystem

The Sundarbans is the world's largest coastal river delta and the largest uninterrupted mangrove ecosystem. A complex socio-ecological setting, coupled with disproportionately high climate-change exposure and severe ecological and social vulnerabilities, has turned it into a climate hotspot requiring well-designed adaptation interventions. We have used the fuzzy cognitive maps (FCM)-based approach to elicit and integrate stakeholders' perceptions regarding current climate forcing, consequent impacts, and effcacy of the existing adaptation measures. We have also undertaken climate modelling to ascertain long-term future trends of climate forcing. FCM-based simulations reveal that while existing adaptation practices provide resilience to an extent, they are grossly inadequate in the context of providing future resilience. Even well-planned adaptations may not be entirely transformative in such a fragile ecosystem. It was through FCM-based simulations that we realised that a coastal river delta in a developing nation merits special attention for climate-resilient adaptation planning and execution. Measures that are likely to enhance adaptive capabilities of the local communities include those involving gender-responsive and adaptive governance, human resource capacity building, commitments of global communities for adaptation financing, education and awareness programmes, and embedding indigenous and local knowledge into decision making. © 2019 by the authors

Application of Fuzzy Cognitive Maps to water demand prediction

This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with the use of real coded genetic algorithms, are described. In this study, a new structure optimization genetic algorithm for fuzzy cognitive maps learning is proposed for automatic construction of FCM applied to time series prediction. The proposed learning methodologies are based on an FCM reconstruction procedure using historical time series. The main contribution of this study is the analysis of the use of FCMs with their learning algorithms based on the multi-step gradient method (MGM) and other population-based methods to predict water demand. The performance of learning algorithms is presented through the analysis of real data of daily water demand and the corresponding prediction. The multivariate analysis of historical water demand data is held for five variables, mean and high temperature, precipitation, wind speed and touristic activity. Simulation results were obtained with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. Through the experimental analysis, we demonstrate the usefulness of the new proposed FCM learning algorithm in water demand prediction, by calculating the known prediction errors. The advantage of the optimization genetic algorithm structure is its ability to select the most significant relations between concepts for prediction. © 2015 IEEE

From undirected structures to directed graphical lasso fuzzy cognitive maps using ranking-based approaches

Fuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to their capabilities in modelling and decision making for complex problems. However, learning FCM models automatically from data without any expert knowledge and/or historical data remains a considerable challenge. For our research, we use the estimated weight matrix from the graphical lasso (glasso) method with the EBIC regulation technique. Particularly, the glasso is a technique originated from machine learning which is used to model a problem by learning the weight matrix directly from a dataset. Moreover, the relationships are expressed by conditional independence among two nodes after conditioning on all the other nodes of the graph. However, the challenging task in this study is the investigation of the suitable transformation of the weight matrix from a symmetric matrix to asymmetric in order to determine the directions of the edges among the concepts and construct the glassoFCM model. For this reason, statistical comparisons are applied to examine if there are significant differences in the value of the output concept when the input concepts are rearranged according to four different cases. The whole approach was implemented in a business intelligence problem of evaluating the willingness of the employees to work in Belgian companies. © 2020 IEEE